With the development of science and technology as well as Internet technology, an increasing number of systems that provide services for users (referred to as user service systems) have emerged, which provide rich and colorful services for the users in various fields. A smart TV system is a product formed under the Internet wave impact, with the purpose of bringing in more convenient experience to the users, which has become a TV trend at present.
With the development of smart TVs, TV sets no longer only have a simple function of watching fixed TV programs, but own more and more functions and content with user experiences, such as, film and video, applications, games and so on, and in order to increase user loyalty of the smart TVs, the TV sets generally also have some additional service functions, such as a service content recommendation function. At present, there are three main manners of recommendation adopted:
1) recommending fixed content to a user in a unified way;
2) speculating the user' mood by detecting physical signs (heart rate, body temperature and so on) of the user and performing simple calculation or matching, or determining the current mood and state of the user through facial recognition and voice recognition, and implementing recommendation on the basis of the above; and
3) collecting and extracting lots of historical operation data of the user and pre-establishing a user operation model, extracting operation features, and performing matching according to the model when the user performs an actual operation.
In actual applications, the above three manners all have their own defects, which are described respectively in the following.
1) This manner belongs to a static recommendation manner, which, due to not using the user's behavior and state into account, does not have a high recommendation conversion rate and cannot provide a service that makes the user satisfied.
2) This manner is greatly affected by the physique of the user or the environment where the TV set is located and a distance between the user and the TV set, resulting in that the user state is not determined accurately, and recommendation implemented on this basis is naturally difficult to make the user satisfied; in addition, hardware such as a sensor camera is also required, which increases the cost of the whole service system.
3) Since one TV set often has many users (shared by multiple family members), and the pre-established model is not specific to a certain particular user, matching a real-time operation state of a certain user with the model and accordingly implementing recommendation generally cannot bring good use experience to the user.